Data processing system communicating with a map data processing system to generate a display of one or more segments of one or more vehicle routes

ABSTRACT

Systems and methods are disclosed for generating a display of a navigation map. The system may comprise a historical data source device having, for example, a historical data source computer and a database storing historical data associated with one or more of vehicle accident data, traffic data, vehicle volume data, vehicle density data, road characteristic data, or weather data. The system may comprise a map data processing device having a map data processing computer and memory storing computer-executable instructions that, when executed by the map data processing computer, cause the map data processing device to, for example, determine, based on a location determining device, a location of a vehicle. The map data processing system may determine one or more historical factors based on the location of the vehicle. The map data processing system may receive, from the historical data source device and for the location, historical data associated with the one or more historical factors. Based on the location of the vehicle, one or more real time factors and real time data associated with the one or more real time factors may be calculated. The map data processing system may calculate, using the one or more historical factors and the one or more real time factors, a navigation score for each segment of a route from the location to a destination location. The map data processing system may determine one or more colors for each navigation score and/or generate a display of a navigation map comprising the one or more colors.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of pending U.S. patent applicationSer. No. 16/023,196, filed Jun. 29, 2018, which is a continuation ofU.S. patent application Ser. No. 15/409,693 (now U.S. Pat. No.10,036,650), filed Jan. 19, 2017 and entitled “Data Processing SystemCommunicating with a Map Data Processing System to Generate a Display ofOne or More Segments of One or More Vehicle Routes,” which is acontinuation of U.S. patent application Ser. No. 15/182,920 (now U.S.Pat. No. 9,581,461), filed Jun. 15, 2016 and entitled “Data ProcessingSystem Communicating with a Map Data Processing System to Generate aDisplay of One or More Segments of One or More Vehicle Routes,” whichclaims priority to U.S. Provisional Patent Application Ser. No.62/274,888, filed Jan. 5, 2016 and entitled “Live Risk Map,” and to U.S.Provisional Patent Application Ser. No. 62/274,835, filed Jan. 5, 2016and entitled “Multi-Dimensional Risk Scoring Model.” U.S. patentapplication Ser. No. 15/409,693 is also a continuation of U.S. patentapplication Ser. No. 15/182,955 (now U.S. Pat. No. 9,915,543), filedJun. 15, 2016 and entitled “Data Processing System Communicating with aMap Data Processing System to Determine or Alter a Navigation Path Basedon One or More Road Segments,” which claims priority to U.S. ProvisionalPatent Application Ser. No. 62/274,888, filed Jan. 5, 2016 and entitled“Live Risk Map,” and to U.S. Provisional Patent Application Ser. No.62/274,835, filed Jan. 5, 2016 and entitled “Multi-Dimensional RiskScoring Model.” Each of the aforementioned applications is hereinincorporated by reference in its entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to computing systemsgenerating dynamic or risk maps by accessing real time data and/orhistorical data from various data sources. A data processing system maycommunicate with a map data processing system to generate a display ofone or more segments of one or more vehicle routes, such as using one ormore colors for one or more segments of the route(s).

BACKGROUND

Mapping services that display traffic conditions and driving routes areknown. However, these existing mapping services do not display enoughinformation for some use cases.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to systems, methods, and computingdevices configured for generating a display of a navigation map. Thesystem may comprise a historical data source device having, for example,a historical data source computer and a database storing historical dataassociated with one or more of vehicle accident data, traffic data,vehicle volume data, vehicle density data, road characteristic data, orweather data. The system may comprise a map data processing devicehaving a map data processing computer and memory storingcomputer-executable instructions that, when executed by the map dataprocessing computer, cause the map data processing device to, forexample, determine a location of a vehicle based on a locationdetermining device. The location of the vehicle may be received from thelocation determining device of a mobile computing device and/or thevehicle. The map data processing system may determine one or morehistorical factors based on the location of the vehicle. The map dataprocessing system may receive, from the historical data source deviceand for the location, historical data associated with the one or morehistorical factors. Based on the location of the vehicle, one or morereal time factors and real time data associated with the one or morereal time factors may be calculated. The map data processing system maycalculate, using the one or more historical factors and the one or morereal time factors, a navigation score for each segment of a route fromthe location to a destination location. The map data processing systemmay determine one or more colors for each navigation score and/orgenerate a display of a navigation map comprising the one or morecolors.

In some aspects, the memory may store computer-executable instructionsthat, when executed by the map data processing computer, cause the mapdata processing device to recalculate at least one of the navigationscore in response to an update to the real time data. Additionally oralternatively, the memory may store computer-executable instructionsthat, when executed by the map data processing computer, cause the mapdata processing device to determine that the navigation score for asegment of the route increases by more than a threshold value. Inresponse to determining that the navigation score for the segment of theroute increases by more than the threshold value, a new color for thesegment of the route may be determined and/or the display of thenavigation map may be updated with the new color for the segment of theroute. In some aspects, the map data processing device may determine acharacteristic for the segment of the route in response to determiningthat the navigation score for the segment of the route increases by morethan the threshold value and/or update the display of the navigation mapwith the characteristic for the segment of the route.

In some aspects, determining the one or more colors for each navigationscore may be based on a comparison of the navigation score for thesegment of the route with a historical navigation score for the segmentof the route. In additional aspects, the display of the navigation mapmay comprise a display of a user-selectable graphical user interface(GUI) element for a segment. The display of the navigation map may beconfigured to display one or more of the historical factors or the realtime factors used to calculate the navigation score for the segment inresponse to a selection of the user-selectable GUI element.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram illustrating various example components of a riskmap generation system according to one or more aspects of thedisclosure.

FIG. 3 is a flow diagram illustrating an example method of generating adynamic risk map according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. In addition, aspects may take the form ofa computing device configured to perform specified actions. Furthermore,such aspects may take the form of a computer program product stored byone or more computer-readable storage media having computer-readableprogram code, or instructions, embodied in or on the storage media. Anysuitable computer readable storage media may be utilized, including harddisks, CD-ROMs, optical storage devices, magnetic storage devices,and/or any combination thereof. In addition, various signalsrepresenting data or events as described herein may be transferredbetween a source and a destination in the form of electromagnetic wavestraveling through signal-conducting media such as metal wires, opticalfibers, and/or wireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device 101 in a liverisk map generation system 100 that may be used according to one or moreillustrative embodiments of the disclosure. The computing device 101 mayhave a processor 103 for controlling overall operation of the computingdevice 101 and its associated components, including RAM 105, ROM 107,input/output module 109, and memory unit 115. The computing device 101,along with one or more additional devices (e.g., terminals 141, 151) maycorrespond to any of multiple systems or devices, such as live risk mapgeneration devices or systems, configured as described herein forreceiving vehicle data, real time data, and historical data, and usingthe data to generate live risk maps.

Input/Output (I/O) module 109 may include a microphone, keypad, touchscreen, and/or stylus through which a user of the computing device 101may provide input, and may also include one or more of a speaker forproviding audio input/output and a video display device for providingtextual, audiovisual and/or graphical output. Software may be storedwithin memory unit 115 and/or other storage to provide instructions toprocessor 103 for enabling device 101 to perform various functions. Forexample, memory unit 115 may store software used by the device 101, suchas an operating system 117, application programs 119, and an associatedinternal database 121. The memory unit 115 includes one or more ofvolatile and/or non-volatile computer memory to storecomputer-executable instructions, data, and/or other information.Processor 103 and its associated components may allow the computingdevice 101 to execute a series of computer-readable instructions toreceive vehicle data, real time data, and historical data, process thedata, and use the data to generate live risk maps.

The computing device 101 may operate in a networked environment 100supporting connections to one or more remote computers, such asterminals/devices 141 and 151. Live risk map generation computing device101, and related terminals/devices 141 and 151, may include devicesinstalled in vehicles, mobile devices that may travel within vehicles,or devices outside of vehicles that are configured to receive andprocess real time and historical data. Thus, the computing device 101and terminals/devices 141 and 151 may each include personal computers(e.g., laptop, desktop, or tablet computers), servers (e.g., webservers, database servers), vehicle-based devices (e.g., on-boardvehicle computers, short-range vehicle communication systems, sensorsand telematics devices), or mobile communication devices (e.g., mobilephones, portable computing devices, and the like), and may include someor all of the elements described above with respect to the computingdevice 101. The network connections depicted in FIG. 1 include a localarea network (LAN) 125 and a wide area network (WAN) 129, and a wirelesstelecommunications network 133, but may also include other networks.When used in a LAN networking environment, the computing device 101 maybe connected to the LAN 125 through a network interface or adapter 123.When used in a WAN networking environment, the device 101 may include amodem 127 or other means for establishing communications over the WAN129, such as network 131 (e.g., the Internet). When used in a wirelesstelecommunications network 133, the device 101 may include one or moretransceivers, digital signal processors, and additional circuitry andsoftware for communicating with wireless computing devices 141 (e.g.,mobile phones, short-range vehicle communication systems, vehiclesensing and telematics devices) via one or more network devices 135(e.g., base transceiver stations) in the wireless network 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, Wi-Fi,and WiMAX, is presumed, and the various computing devices and risk mapgeneration system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the computingdevice 101 may include computer executable instructions (e.g., real timeand historical data analysis programs, risk map generation algorithms,and the like) for receiving real time and historical data, generatinglive risk maps, and performing other related functions as describedherein.

FIG. 2 is a diagram illustrating various example components of a riskmap generation system 200 according to one or more aspects of thedisclosure. The risk map generation system 200 may include a vehicledata source 210 (e.g., a vehicle and its associated sensors 212 or amobile device located within the vehicle), a mobile computing device214, other vehicle data sources (not illustrated), a real time datasource 220, a historical data source device 230, a map data processingdevice (e.g., a risk map server) 250, and additional related components.Each component shown in FIG. 2 may be implemented in hardware, software,or a combination of the two. Additionally, each component of the riskmap generation system 200 may include a computing device (or system)having some or all of the structural components described above forcomputing device 101.

The system 200 may include a vehicle data source 210, containing some orall of the hardware/software components as the computing device 101depicted in FIG. 1 . The vehicle data source 210 may comprise a vehiclehaving a vehicle computer 211 and vehicle sensors 212. While one vehicleis illustrated, vehicle data may be collected from a plurality ofvehicles. The vehicle sensors 212 may sense driving data, such speed,acceleration, rotation, braking, etc. The vehicle sensors 212 may alsocomprise a location determining device, such as a Global PositioningSystem (GPS), cellular position device, or any other positiondetermining device, used to determine the location of the vehicle 210.The vehicle computer 211 may collect the driving data from the vehiclesensors 212 and send the data to the risk map server 250 for processing,as will be described in further detail below.

The system 200 may include a mobile computing device 214, containingsome or all of the hardware/software components as the computing device101 depicted in FIG. 1 . The mobile computing device 214 (e.g., a mobilephone, a tablet, a wearable, etc.) may be located inside of a vehicleand thus be configured to sense and monitor driving data. For example,the sensors 215 of the mobile computing device 214 may sense drivingdata, such speed, acceleration, rotation, braking, etc. The sensors 215may also comprise a location determining device, such as GPS, cellularetc., used to determine the location of the mobile computing device 214(and consequently vehicle 210). The mobile computing device 214 maycollect the driving data from the sensors 215 and send the data to therisk map server 250 for processing, as will be described in furtherdetail below.

The system 200 may include a real time data source 220, containing someor all of the hardware/software components as the computing device 101depicted in FIG. 1 . Real time data source 220 may comprise a real timedata source computer 221 for receiving and/or processing real time data,as will be described in further detail below. The real time data source220 may also comprise a database 222 used to store the real time datacollected by the real time data source computer 221. The real time datasource computer 221 may transmit the real time data to the risk mapserver 250 for processing, as will be described in further detail below.

The system 200 may include a historical data source 230, containing someor all of the hardware/software components as the computing device 101depicted in FIG. 1 . Historical data source 230 may comprise ahistorical data source computer 231 for receiving and/or processinghistorical data, as will be described in further detail below. Thehistorical data source 230 may also comprise a database 232 used tostore the historical data collected by the historical data sourcecomputer 231. The historical data source computer 231 may transmit thehistorical data to the risk map server 250 for processing, as will bedescribed in further detail below.

The system 200 may include a risk map server 250, containing some or allof the hardware/software components as the computing device 101 depictedin FIG. 1 . The risk map server 250 may include hardware, software, andnetwork components to receive data from one or more data sources, suchas the vehicle data source 210 (e.g., via vehicle computer 211), mobilecomputing device 214, real time data source 220, historical data source230, and other data sources. The risk map server 250 may include adatabase 252, which may include additional data for the risk map server250 to process to, for example, generate one or more risk maps. The riskmap computer 251 may analyze data received from the various datasources. The risk map server 250 may initiate communication with and/orretrieve data from the vehicle data source 210, mobile computing device214, real time data source 220, historical data source 230 wirelessly,or by way of separate computing systems over one or more computernetworks (e.g., the Internet).

FIG. 3 is a flow diagram illustrating an example method of generating adynamic risk map according to one or more aspects of the disclosure. Thesteps illustrated in FIG. 3 may be performed by a computing device, suchas the risk map computer 251.

In step 305, the computing device may determine historical factors fordetermining risk value(s). Exemplary historical factors and/or datainclude historic accident historic accident data (e.g., type andseverity of accidents based on location and environmental conditions),historic traffic (e.g., congestion data), historic vehicle volume and/ordensity, road characteristics, and historic weather data.

In step 310, the computing device may determine data source(s) havingdata corresponding to historical factors and access the historical datafrom the data source(s). An exemplary data source includes a datarepository at the risk map server 250 (e.g., the database 252) orotherwise directly available to the risk map computer 251. For example,the database 252 may store accident claims data, which may indicate theday and time of an accident, the location of the accident, and otherinformation describing the accident. Other data sources include thirdparty or partner repositories. For example, the risk map computer 251may access publically available information, such as from one or moredepartment of transportation (DOT) data sources.

In step 315, the computing device may determine real time factors fordetermining risk value(s). Numerous real time factors exist, and thesereal time factors may affect how the computing device determines riskscores (as will be described below in further detail). Real time factorsmay comprise real time traffic flow information, such as vehicle speed,vehicle density, vehicle volume, and the like. Real time factors mayalso include weather data, such as whether there is precipitation (e.g.,rain or snow) at the location of the vehicle and the rate ofprecipitation (e.g., 1 inch per hour). The weather data may alsoindicate changes in the weather, whether the changes occurred in thenear past, are occurring in the present, or are forecasted to occur inthe future. Real time factors may also indicate accidents, road closures(e.g., emergency road closures), construction zones (e.g., a few minutesor a few hours ago), and the like. The real time factors may includedata indicating planned events for particular locations, such asmarathons, concerts, etc.

Real time factors may also include real time driving data. For example,real time data may indicate a driver's behavior patterns (orpreferences), such as braking patterns, acceleration patterns, speedpatterns, and the like. Other driving data may include GPS trace data,driver ID, time, speed, heading, etc. The computing device may collectthis data from sensors on board a vehicle (e.g., vehicle behaviorsensors) or mobile devices within vehicles (e.g., a mobile phone). Thedata collected may comprise, for example, consumer or GPS probe data.Real time data may also be collected from autonomous driving platforms,such as autonomous vehicles or data sources monitoring autonomousvehicles. Real time data sources may also indicate driver coaching orsafety guidance attributes.

In step 320, the computing device may determine data sources having datacorresponding to real time factors and accesses the real time data. Datasources may include, for example, vehicles, mobile devices locatedwithin vehicles, on road sensors or other traffic devices (e.g.,counters, cameras, traffic lights, speed cameras, and the like), weatherservice data sources, or other real time data sources. For example, athird party real time data source and computing device may collectand/or aggregate data from multiple real time data sources and send thedata to the risk map server 250 for processing.

In step 325, the computing device may determine navigation score(s)(e.g., risk score(s)) using the real time data and/or the historicaldata. Vehicles may present various types of risks, and a risk score maybe generated for each type of risk. The computing device may alsogenerate an aggregate or combined risk score that factors in two or moretypes of risk. One type of risk is the risk of driving, which mayinclude accidents, congestion, and the like. Another type of risk maycomprise non-driving use risks. Risk scores may indicate the level ofrisk to, for example, bicycles near the vicinity of one or morevehicles, motorcycles, pedestrians, farm equipment, etc. There may be arisk of a vehicle running into people or things. For example, the riskto pedestrians may increase as the number or density of pedestriansincreases in a particular area.

Another risk may be the risk of using a roadway network in an aggregatedway, such as deploying fleets of vehicles effectively. The risk tofleets may vary based on traffic conditions, weather conditions, vehicledensity, and the like. Ride sharing services or other transportationservices may use this risk information to route and deploy theirvehicles in a safer and more efficient manner than competition. Adelivery service may use this information to handle deliveries moreefficiently.

Another risk may be reduced using the risk data (e.g., risk score) tooptimize road infrastructure usage from DOTs, other governmentauthorities, or third parties. For example, construction companies orgovernment entities may use the risk data to determine when to performconstruction on buildings or roads or build a new bike lane, which mayresult in street closures. City planners may similarly use this data todetermine whether to construct new bike lanes, how to structure trafficlights, and when or how to deploy traffic management personnel.

The risk score may comprise a multi-dimensional risk score, such as themulti-dimensional risk score described in U.S. Provisional PatentApplication Ser. No. 62/274,835, filed Jan. 5, 2016 and entitled,“Multi-Dimensional Risk Scoring Model,” and/or the multi-dimensionalrisk score described in U.S. patent application Ser. No. 15/182,955entitled “Data Processing System Communicating With A Map DataProcessing System To Determine Or Alter A Navigation Path Based On OneOr More Road Segments” and filed Jun. 15, 2016. The related applicationsare herein incorporated by reference in their entirety.

In step 330, the computing device may determine risk pricing (e.g.,insurance rates) and/or generate safety management recommendations(e.g., safety products to offer) based on the risk score(s) or otherrisk data. Risk pricing may comprise a consumer risk score, which may besimilar to credit scores. The consumer risk score may be based on one ormore of the factors described herein. The risk data may be used toreduce commercial insurance rates by a certain percentage (e.g., 10%) ifthe consumer uses the risk management services described herein.

The risk pricing data determined by the computing device may be used togenerate an in-car insurance marketplace. Insurance companies may bid onconsumers' business. As an OEM, the revenue share from the insurancemarketplace may increase the value of a vehicle. The risk pricing datamay be used to determine autonomous car hybrid coverage. For example,the risk score may be used to determine product liability or commercialpremiums. Whether the car is driving, or the driver is at the wheel, theinsurance company may have the vehicle covered. Manufacturers ofautonomous vehicles may build the next version of self-driving cars withconfidence because of the liability insurance.

The risk data may be used for managing fleet risk. For example, thesystem described herein may generate and send a risk-informed routeoptimization and driver safety management to fleet companies, large orsmall. That is, the routing may be risk-informed and based on driverscores. An insurance company may offer insurance friendly routeoptimization.

Consumer solutions for road safety may also be provided based on therisk data. Road safety solutions may include safe routing, parking,driving, informed texting and driver scoring/coaching, and the like.

The risk scores may be used by the computing device to manage carsharing risk. For a ride or car sharing service, drivers may keepcustomers safer by following safe routes (e.g., routes with lower riskscores) during trips that are recommended by the system.

The computing device may also determine real estate location riskscoring, including for both traditional and online real estate brokers.Lenders may pay for risk-related information, such as traffic volumes,accident history, and the like. Prospective buyers of real estate assetsmay also use this information. A commercial lender may be able toincorporate more comprehensive risk location profile data into financingmodels for borrowers. First time home buyers may access a location riskprofile for the area surrounding a prospective home to be purchases,which may be as valuable as school ratings for the area.

City planners and/or government agencies may also be able to use therisk data. For example, the risk data may be used by governmenttransportation management to optimize infrastructure, determine whichroadways to prioritize fixing, and to learn more about dangers on theroad because of alerts received from the city.

The risk data may also be used to optimize delivery of service,including a roadside assistance service. For example, delivery ofservice may be optimized by positioning vehicles (e.g., tow trucks) inhigh risk areas. Drivers needing assistance may be surprised how quicklya tow truck arrives. As a tow truck operator, the risk data may be usedto increase tow truck productivity by a certain amount, such as 20%.

As will be described below in further detail, the risk data (e.g., riskscores) may be presented to users as one or more risk maps.

In step 335, the computing device may determine one or morecharacteristics of a dynamic risk map to be generated. The risk map mayindicate, among other characteristics, risk scores for one or moresegments of a road, such as on a route from an origin location to adestination location. As will be described in further detail below, therisk map may comprise a dynamic risk map. The risk map may display thecausal effect on risk and one or more of the factors outlined above. Forexample, the data to be generated and displayed, in real time, mayinclude map service data, such as routing data, analytics, updates, andother map data. The data displayed may also include location, time,behavior, and/or environment data.

The computing device may determine characteristics of the map todisplay, such as the causal effect on real time risk on a live map. Forexample, traffic congestion and/or navigation or risk scores may berepresented by different colors on the dynamic map, such as red (heavycongestion or high risk), white (medium congestion or medium risk), orblue (low congestion or low risk). Any number of colors may be displayedon the dynamic map, and each color may represent a different navigationor risk score. The computing device may change the colors on the map astraffic congestion and/or risk scores change. Risk scores and trafficcongestion may be represented in other ways, such as hot spots (e.g.,gradient colors similar to weather Doppler radar views). Additionally oralternatively, lane level risks may be highlighted on the dynamic map.

The computing device may determine to use other visualizationtechniques. For example, the dynamic map may comprise one or morebubbles (or other prompts containing data such as text) that pop up onthe map. The prompts may include various data, such as the risk scorefor a particular roadway segment on the map, historical factors orhistorical data used by the computing device to determine the riskscore, real time factors or real time data used to determine the riskscore, risk pricing or safety management information, and the like. Insome aspects, the data prompts may automatically disappear on the fly asnew information (e.g., real time information) surfaces.

The computing device may also use drilldown displays. For example, thefirst bubble displayed may indicate the risk score for a segment. If theuser selects the risk score or other GUI element in the bubble, the mapmay display which factors (e.g., historical and/or real time factors)and/or which data (e.g., historical and/or real time data) were used togenerate the risk score. As previously explained, toggles may beprovided so that a user may turn on or turn off each factor used tocalculate a risk score. These toggles may also be displayed on thedynamic map, such as in one of the drilldown displays.

The live risk map may display how (quantified) risk moves across themap. A risk flow may be displayed, and the risk flow may indicate howrisk flows along the road segments as traffic congestion unfolds orbacks up on the road. For example, a wave form visual construct may bedisplayed and may be based on a mathematical model that applies to roadrisk flow. The map may display a wave moving jam and how trafficaccidents may move like a waveform.

The dynamic may comprise a dial or other GUI element used to movebackwards and forwards in time. As time changes according to the dial,the map may change the risk scores displayed according to the time. Forexample, a user may move the time on map via the dial (e.g., in 10minute increments), and the user may see the risk scores move up anddown in a waveform-like manner based on computations performed by thecomputing device.

The dynamic map may include a predictive view that may be useful fordetermining when to deploy fleets or when to drive. For example, thepresent time may be 10:40 AM, and the user may desire to see how therisk fluctuates between 10:45 AM and noon to determine when to deploy afleet (e.g., a ride sharing fleet, a delivery truck fleet, etc.). Aspreviously explained, drilldown information may be displayed, and theuser may be able to toggle each factor considered in determining therisk score. Thus, the user may determine which factors are moreimportant and which factors are less important when deciding to deploy afleet.

In step 340, the computing device may generate one or more dynamic riskmap based on risk score(s). The risk map may comprise a representationof risk (e.g., risk score or factors) based on location, weather,traffic, context and other variables. The dynamic risk map may includeany one or more of the characteristics described above with reference tostep 335, such as colors, drilldown displays, toggles, etc. In otherwords, the system described herein may generate a dynamic map thatdemonstrates the causal effect on risk of various factors, includingreal-time factors such as weather, traffic, road closures, construction,planned closures, and the like. Various data attributes and real-timevalues may be displayed on the dynamic map.

Maps may be generated for individual users, such as commuters before orafter they begin driving. The maps may also be generated forbusiness-to-business users, such as a logistics company, a ride sharingcompany, and the like. As described above, the map and its associatedinformation may be used to determine when to deploy a fleet, such as todeliver packages.

In step 345, the computing device may determine whether there is anupdate or other change to the real time data. If so (step 345: Y), thecomputing device may proceed to step 315 to determine real time factorsfor recalculating a risk score (or alternatively to step 320 to retrievethe updated data directly). In other words, the map may comprise a live,dynamic risk map that updates in real time based on changes to real timedata and potentially based on changes to historical data.

In some aspects, the computing device may use thresholds to determinewhether to update the data on the dynamic risk map. The thresholds maybe predetermined or algorithmically determined. If a threshold isexceeded, the computing device may determine to update the risk map. Forexample, if traffic at a particular location is backed up by certainamount (e.g., above a threshold amount), the computing device maydetermine to update the risk map data. As another example, if trafficexceeds a threshold amount and the computing device determines that oneor more other factors is satisfied (e.g., if there is snow on the road),the computing device may update the risk map data. If, on the otherhand, a threshold is not exceeded, the computing device may determinenot to update the risk map.

Whether the computing device updates the risk map may be based on therisk score exceeding a risk score threshold. For example, if a riskscore increases by 40% at a particular location (e.g., a road segment),the computing device may determine to update the map. As anotherexample, if the risk score is 40% higher than a historical average riskscore for the same location, the computing device may update the riskmap. The computing device may also consider one or more other factors,such as if snow, rain, and the like is present before determining toupdate the data on the map. Colors for each navigation score may bebased on a comparison of the navigation score for the segment of theroute with a historical navigation score for the segment of the route.One color on the map may indicate that the current risk level is on parwith a historical risk score average. Another color may indicate thatthe current risk level is below a historical average by a certainthreshold. A third color may indicate that the current risk level isabove a historical average by a certain threshold.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

What is claimed is:
 1. A system comprising: a map data processing devicecomprising: a map data processing computer; and memory storingcomputer-executable instructions that, when executed by the map dataprocessing computer, cause the map data processing device to: receive,from a historical data source including a historical data sourcecomputer and a database, historical data associated with one or morehistorical factors and with a location of a vehicle; receive real timedata associated with one or more real time factors and with the locationof the vehicle; determine, based on the historical data and the realtime data, a navigation score for each segment of at least a portion ofa route from the location of the vehicle to a second location; determineone or more characteristics for each navigation score based on acomparison of the navigation score for the segment of the at least theportion of the route with a historical navigation score for the segmentof the at least the portion of the route; and output a display of anavigation map comprising the one or more characteristics, wherein thenavigation map is dynamically updated based on updated real time data.2. The system of claim 1, wherein a first navigation score for a firstsegment indicates a risk associated with the first segment.
 3. Thesystem of claim 1, wherein the navigation map is dynamically updatedfurther based on updated historical data.
 4. The system of claim 1,wherein the updated real time data comprises weather data.
 5. The systemof claim 1, wherein the navigation map further comprises traffic data.6. The system of claim 1, wherein each characteristic on the navigationmap is displayed in a different color.
 7. The system of claim 6, whereinupdating the navigation map comprises updating the different colors ofeach characteristic.
 8. A method comprising: receiving historical dataassociated with one or more historical factors and with a location of avehicle; receiving real time data associated with one or more real timefactors and with the location of the vehicle; determining, based on thehistorical data and the real time data, a navigation score for eachsegment of at least a portion of a route from the location of thevehicle to a second location; determining one or more characteristicsfor each navigation score based on a comparison of the navigation scorefor the segment of the at least the portion of the route with ahistorical navigation score for the segment of the at least the portionof the route; and outputting a display of a navigation map comprisingthe one or more characteristics, wherein the navigation map isdynamically updated based on updated real time data.
 9. The method ofclaim 8, wherein a first navigation score for a first segment indicatesa risk associated with the first segment.
 10. The method of claim 8,wherein the navigation map is dynamically updated further based onupdated historical data.
 11. The method of claim 8, wherein the updatedreal time data comprises weather data.
 12. The method of claim 8,wherein the navigation map further comprises traffic data.
 13. Themethod of claim 8, wherein each characteristic on the navigation map isdisplayed in a different color.
 14. The method of claim 13, whereinupdating the navigation map comprises updating the different colors ofeach characteristic.
 15. A map data processing device comprising: a mapdata processing computer; and memory storing computer-executableinstructions that, when executed by the map data processing computer,cause the map data processing device to: receive historical dataassociated with one or more historical factors and with a location of avehicle; receive real time data associated with one or more real timefactors and with the location of the vehicle; determine, based on thehistorical data and the real time data, a navigation score for one ormore segments of at least a portion of a route from the location of thevehicle to a second location; determine one or more characteristics foreach navigation score based on a comparison of the navigation score forthe segment of the at least the portion of the route with a historicalnavigation score for the segment of the at least the portion of theroute; and output a display of a navigation map comprising the one ormore characteristics, wherein the navigation map is dynamically updatedbased on updated real time data.
 16. The map data processing device ofclaim 15, wherein a first navigation score for a first segment indicatesa risk associated with the first segment.
 17. The map data processingdevice of claim 15, wherein the navigation map is dynamically updatedfurther based on updated historical data.
 18. The map data processingdevice of claim 15, wherein the updated real time data comprises weatherdata.
 19. The map data processing device of claim 15, wherein thenavigation map further comprises traffic data.
 20. The map dataprocessing device of claim 15, wherein each characteristic on thenavigation map is displayed in a different color, and wherein updatingthe navigation map comprises updating the different colors of eachcharacteristic.